app.py

import gradio as gr
import openai
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import sys
import os
from IPython.display import Markdown, display

# Define API key globally
api_key = "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"

def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex.from_documents(documents)

    index.save_to_disk('index.json')

    return index

def ask_ai(question, api_key):
    # Use global API key variable
    os.environ["OPENAI_API_KEY"] = api_key
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(question, response_mode="compact")
    return response.response

construct_index("data")

# Create Gradio interface to prompt for API key
api_key_input = gr.inputs.Textbox(label="Enter your OpenAI API key:")

# Define the interface
iface = gr.Interface(fn=ask_ai, inputs=["text", api_key_input], outputs="text" ,title="Jim's Chatbot")

# Start the interface
iface.launch()